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Cognitive Bias and Critical Thinking in Open Source Intelligence - PowerPoint PPT Presentation

Cognitive Bias and Critical Thinking in Open Source Intelligence (OSINT) Benjamin Brown Akamai Technologies Security Architecture * Law Enforcement Engagement * Systems Safety * Threat Intelligence * Security Research - Novel Attack


  1. Application

  2. Application - Report - Clearly Define * Goals * Sources * Audience * Limitations & Assumptions ...

  3. Application METHODOLOGY Verizon, Trend Micro, Prolexic, Recorded Future

  4. Further Research

  5. Further Research - Context * Chrononarcissism and historical context * Socio-cultural and economic context - Unknown unknowns and blindspots - Data originator biases / data supply-chain pressures - Baked-in Bias for OSINT automation tools - Analysis of competing hypotheses (ACH)

  6. Suggested Reading - “Psychology of Intelligence Analysis” , Richards J. Heuer, Jr. - “A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis” , US Government - “Judgement in Managerial Decision Making” , Max H. Bazerman and Don Moore

  7. Contact Me bbrowntalks@gmail.com

  8. Cognitive Bias and Critical Thinking in Open Source Intelligence (OSINT)

  9. Benjamin Brown Akamai Technologies Security Architecture * Law Enforcement Engagement * Systems Safety * Threat Intelligence * Security Research - Novel Attack Vectors - Multilayered Attacks

  10. Coming To Terms - Cognitive Biases - Open Source Intelligence - Intelligence Analysis - Metacognition - Critical Thinking - Frameworks for Structured Analysis

  11. The terms may layout a ‘So’, but we also need a ‘So What’

  12. Why We Care Actionable Intelligence - Accurate conclusions - Properly framed Cognitive Biases (faulty heuristics) - Can lead to inaccurate conclusions Metacognition and Critical Thinking - Recognize and correct for cognitive biases - Arrive at more accurate solutions more often

  13. Why We Care Bad Data Biased Analysis False Conclusions Bad Intelligence

  14. Why We Care Think Bay of Pigs

  15. Open Source Intelligence (OSINT)

  16. Defining OSINT "Intelligence produced from publicly available information" Army Techniques Publication (ATP), “Open-Source Intelligence,” 2-22.9 July 2012.

  17. Defining OSINT Typical Quality of Publicly Available Information: You are relying on public information of variable quality to draw important conclusions that you intend to act upon. You damn well better be able to recognize bad information and bad analysis. Including you own!

  18. Intelligence vs Information - Timely - Relevant VS - Actionable Information may look interesting, but is it really useful?

  19. OSINT Sources - Search engines - Social networks - Communication services - E-commerce site profiles - Business / tax records - Media

  20. OSINT Tools - Recon-ng - Cree.py - ExifTool - fierce.pl - theHarvester - TAPIR - Maltego - DNSRecon

  21. Defining Cognitive Bias

  22. Defining Cognitive Bias Note: Emotional, intrinsically cultural, spiritual, or faith-based biases are out of scope.

  23. Defining Cognitive Bias - Patterns of subjective judgment. - Simplified information processing strategies. - Subconscious mental procedures for processing information.

  24. Defining Cognitive Bias ‘Deduction’ of color / shade Human brain does not see the colors as they are, but ‘deduces’ the shade of grey from other information presented.

  25. Defining Cognitive Bias ‘Deduction’ of color / shade Human brain does not see the colors as they are, but ‘deduces’ the shade of grey from other information presented.

  26. Example Types of Cognitive Bias

  27. Select Biases Confirmation Bias - Seek out evidence that confirms - Self-fulfilling prophecies - Avoid information supporting competing hypotheses.

  28. Select Biases Self-serving Bias - Self-enhancement - Self-preservation - Self-esteem - Social and/or Career Advancement

  29. Select Biases Echo Effect - Information repeated by source after source. * Media telephone * Sources obscured * Bandwagon effect * Promotes group-think

  30. Select Biases Representativeness - Focus on similarities / Neglect differences

  31. Select Biases Representativeness Cont. - Base Rate Neglect Focusing on specific information and neglecting the importance of base rates

  32. Select Biases - Base Rate Neglect Cont. * Deadly Disease ‘X’ * Afflicts .01% of Population * Cheap diagnostic test that finds the disease in 99.5% of those infected, false-positive rate of only 1.95% * Test = Positive (Oh God I’m Dying! 99.5%!!!) ...But Wait, There’s More... Focusing on specific information and neglecting the importance of base rates

  33. Select Biases - Base Rate Neglect Cont. 99.5% likely to find it if you have it. - 1.95% false-positive rate - 1 mill people tested, 100 of which are infected ( .01% ) - Test accurately identifies 99 of them as infected - BUT 19,500 uninfected receive a false-positive (1.95%) Focusing on specific information and neglecting the importance of base rates

  34. Select Biases Availability Bias - “Anecdata” (first or second hand) - Topic’s trend-power - Censorship - Language(s) of collector / analyst - Маскировка (Maskirovka) * Dezinformatsiia

  35. Synthesis

  36. Reddit vs. Boston Bombers

  37. Reddit vs. Boston Bombers “Methodology” - Marathon Photos and Videos * ‘not looking at the race’ - Warped police scanner snippets - Multiple ‘suspects’ * Social media harassment / cyberstalking - Media outlets ran bad info from Reddit

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